Overview

Dataset statistics

Number of variables10
Number of observations674545
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.5 MiB
Average record size in memory80.0 B

Variable types

DateTime1
Categorical1
Numeric8

Alerts

Temperatura is highly correlated with Hora and 1 other fieldsHigh correlation
Temperatura_Aparente is highly correlated with Hora and 2 other fieldsHigh correlation
Radiacion_Solar is highly correlated with Hora and 1 other fieldsHigh correlation
Hora is highly correlated with Temperatura and 3 other fieldsHigh correlation
Humedad is highly correlated with HoraHigh correlation
Precipitacion is highly skewed (γ1 = 70.31982767) Skewed
Zona_Carga is uniformly distributed Uniform
Hora has 28109 (4.2%) zeros Zeros
Precipitacion has 639969 (94.9%) zeros Zeros
Velocidad_Viento has 12368 (1.8%) zeros Zeros
Radiacion_Solar has 334764 (49.6%) zeros Zeros
Nubosidad has 524204 (77.7%) zeros Zeros

Reproduction

Analysis started2022-11-07 03:44:38.409822
Analysis finished2022-11-07 03:44:57.843755
Duration19.43 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Fecha
Date

Distinct4686
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
Minimum2010-01-01 00:00:00
Maximum2022-10-30 00:00:00
2022-11-06T20:44:57.899528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-06T20:44:57.981691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Zona_Carga
Categorical

UNIFORM

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
Obregon
112428 
Guaymas
112428 
Caborca
112428 
Nogales
112428 
Navojoa
112428 

Length

Max length10
Median length7
Mean length7.499914757
Min length7

Characters and Unicode

Total characters5059030
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowObregon
2nd rowObregon
3rd rowObregon
4th rowObregon
5th rowObregon

Common Values

ValueCountFrequency (%)
Obregon112428
16.7%
Guaymas112428
16.7%
Caborca112428
16.7%
Nogales112428
16.7%
Navojoa112428
16.7%
Hermosillo112405
16.7%

Length

2022-11-06T20:44:58.071168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-06T20:44:58.144751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
obregon112428
16.7%
guaymas112428
16.7%
caborca112428
16.7%
nogales112428
16.7%
navojoa112428
16.7%
hermosillo112405
16.7%

Most occurring characters

ValueCountFrequency (%)
a786996
15.6%
o786950
15.6%
r337261
 
6.7%
e337261
 
6.7%
s337261
 
6.7%
l337238
 
6.7%
g224856
 
4.4%
b224856
 
4.4%
N224856
 
4.4%
m224833
 
4.4%
Other values (11)1236662
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4384485
86.7%
Uppercase Letter674545
 
13.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a786996
17.9%
o786950
17.9%
r337261
7.7%
e337261
7.7%
s337261
7.7%
l337238
7.7%
g224856
 
5.1%
b224856
 
5.1%
m224833
 
5.1%
c112428
 
2.6%
Other values (6)674545
15.4%
Uppercase Letter
ValueCountFrequency (%)
N224856
33.3%
O112428
16.7%
C112428
16.7%
G112428
16.7%
H112405
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin5059030
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a786996
15.6%
o786950
15.6%
r337261
 
6.7%
e337261
 
6.7%
s337261
 
6.7%
l337238
 
6.7%
g224856
 
4.4%
b224856
 
4.4%
N224856
 
4.4%
m224833
 
4.4%
Other values (11)1236662
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5059030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a786996
15.6%
o786950
15.6%
r337261
 
6.7%
e337261
 
6.7%
s337261
 
6.7%
l337238
 
6.7%
g224856
 
4.4%
b224856
 
4.4%
N224856
 
4.4%
m224833
 
4.4%
Other values (11)1236662
24.4%

Hora
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.50121786
Minimum0
Maximum23
Zeros28109
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2022-11-06T20:44:58.207047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9219012
Coefficient of variation (CV)0.6018407168
Kurtosis-1.204014689
Mean11.50121786
Median Absolute Deviation (MAD)6
Skewness-0.0001813032955
Sum7758089
Variance47.91271622
MonotonicityNot monotonic
2022-11-06T20:44:58.263610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2328116
 
4.2%
128109
 
4.2%
2228109
 
4.2%
2128109
 
4.2%
2028109
 
4.2%
1928109
 
4.2%
1828109
 
4.2%
1728109
 
4.2%
1628109
 
4.2%
1528109
 
4.2%
Other values (14)393448
58.3%
ValueCountFrequency (%)
028109
4.2%
128109
4.2%
228031
4.2%
328109
4.2%
428109
4.2%
528109
4.2%
628109
4.2%
728109
4.2%
828109
4.2%
928109
4.2%
ValueCountFrequency (%)
2328116
4.2%
2228109
4.2%
2128109
4.2%
2028109
4.2%
1928109
4.2%
1828109
4.2%
1728109
4.2%
1628109
4.2%
1528109
4.2%
1428109
4.2%

Temperatura
Real number (ℝ)

HIGH CORRELATION

Distinct543
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.60702918
Minimum-9.4
Maximum47
Zeros45
Zeros (%)< 0.1%
Negative1405
Negative (%)0.2%
Memory size5.1 MiB
2022-11-06T20:44:58.328949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-9.4
5-th percentile9.5
Q117.8
median24.3
Q329.7
95-th percentile36.2
Maximum47
Range56.4
Interquartile range (IQR)11.9

Descriptive statistics

Standard deviation8.194188555
Coefficient of variation (CV)0.3471079945
Kurtosis-0.361028029
Mean23.60702918
Median Absolute Deviation (MAD)5.9
Skewness-0.2756116589
Sum15924003.5
Variance67.14472607
MonotonicityNot monotonic
2022-11-06T20:44:58.399001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
283730
 
0.6%
303623
 
0.5%
293583
 
0.5%
273469
 
0.5%
27.73462
 
0.5%
28.33459
 
0.5%
28.43433
 
0.5%
263411
 
0.5%
28.23404
 
0.5%
28.63392
 
0.5%
Other values (533)639579
94.8%
ValueCountFrequency (%)
-9.42
< 0.1%
-9.32
< 0.1%
-8.83
< 0.1%
-8.51
 
< 0.1%
-8.11
 
< 0.1%
-7.51
 
< 0.1%
-7.31
 
< 0.1%
-7.12
< 0.1%
-6.91
 
< 0.1%
-6.81
 
< 0.1%
ValueCountFrequency (%)
471
 
< 0.1%
46.81
 
< 0.1%
46.43
< 0.1%
46.21
 
< 0.1%
46.11
 
< 0.1%
464
< 0.1%
45.96
< 0.1%
45.84
< 0.1%
45.74
< 0.1%
45.64
< 0.1%

Temperatura_Aparente
Real number (ℝ)

HIGH CORRELATION

Distinct649
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.16692659
Minimum-18.2
Maximum55
Zeros103
Zeros (%)< 0.1%
Negative2621
Negative (%)0.4%
Memory size5.1 MiB
2022-11-06T20:44:58.603714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-18.2
5-th percentile10
Q117.2
median25.2
Q332.3
95-th percentile42.4
Maximum55
Range73.2
Interquartile range (IQR)15.1

Descriptive statistics

Standard deviation10.20227767
Coefficient of variation (CV)0.4053843296
Kurtosis-0.5596479849
Mean25.16692659
Median Absolute Deviation (MAD)7.6
Skewness0.01927843674
Sum16976224.5
Variance104.0864696
MonotonicityNot monotonic
2022-11-06T20:44:58.676974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.23001
 
0.4%
30.12922
 
0.4%
312882
 
0.4%
30.42877
 
0.4%
30.62865
 
0.4%
29.92863
 
0.4%
30.32857
 
0.4%
29.72829
 
0.4%
30.82819
 
0.4%
31.12810
 
0.4%
Other values (639)645820
95.7%
ValueCountFrequency (%)
-18.21
< 0.1%
-17.91
< 0.1%
-17.61
< 0.1%
-16.81
< 0.1%
-16.61
< 0.1%
-14.91
< 0.1%
-14.22
< 0.1%
-142
< 0.1%
-13.51
< 0.1%
-12.51
< 0.1%
ValueCountFrequency (%)
551
 
< 0.1%
54.61
 
< 0.1%
54.11
 
< 0.1%
541
 
< 0.1%
53.71
 
< 0.1%
53.61
 
< 0.1%
53.51
 
< 0.1%
53.22
 
< 0.1%
53.12
 
< 0.1%
535
< 0.1%

Precipitacion
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct714
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03071824711
Minimum0
Maximum82
Zeros639969
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2022-11-06T20:44:58.756642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.05
Maximum82
Range82
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3194060635
Coefficient of variation (CV)10.3979261
Kurtosis13948.9958
Mean0.03071824711
Median Absolute Deviation (MAD)0
Skewness70.31982767
Sum20720.84
Variance0.1020202334
MonotonicityNot monotonic
2022-11-06T20:44:58.827459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0639969
94.9%
0.061920
 
0.3%
0.071717
 
0.3%
0.081523
 
0.2%
0.091390
 
0.2%
0.051147
 
0.2%
0.11136
 
0.2%
0.111009
 
0.1%
0.12970
 
0.1%
0.13878
 
0.1%
Other values (704)22886
 
3.4%
ValueCountFrequency (%)
0639969
94.9%
0.0174
 
< 0.1%
0.02121
 
< 0.1%
0.03101
 
< 0.1%
0.0495
 
< 0.1%
0.051147
 
0.2%
0.061920
 
0.3%
0.071717
 
0.3%
0.081523
 
0.2%
0.091390
 
0.2%
ValueCountFrequency (%)
822
< 0.1%
411
< 0.1%
28.711
< 0.1%
28.341
< 0.1%
23.391
< 0.1%
22.941
< 0.1%
22.571
< 0.1%
19.321
< 0.1%
18.371
< 0.1%
17.791
< 0.1%

Humedad
Real number (ℝ≥0)

HIGH CORRELATION

Distinct991
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.25697337
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2022-11-06T20:44:58.901565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.7
Q130.4
median49.6
Q369.8
95-th percentile88.7
Maximum100
Range99
Interquartile range (IQR)39.4

Descriptive statistics

Standard deviation23.7512555
Coefficient of variation (CV)0.4725962171
Kurtosis-1.037861699
Mean50.25697337
Median Absolute Deviation (MAD)19.7
Skewness0.08097398452
Sum33900590.1
Variance564.1221377
MonotonicityNot monotonic
2022-11-06T20:44:58.985568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001801
 
0.3%
27.9983
 
0.1%
34.2978
 
0.1%
46.5970
 
0.1%
32.4956
 
0.1%
33.1946
 
0.1%
35.2940
 
0.1%
33.4939
 
0.1%
28.7939
 
0.1%
28.8939
 
0.1%
Other values (981)664154
98.5%
ValueCountFrequency (%)
13
 
< 0.1%
1.13
 
< 0.1%
1.23
 
< 0.1%
1.37
< 0.1%
1.47
< 0.1%
1.52
 
< 0.1%
1.63
 
< 0.1%
1.78
< 0.1%
1.87
< 0.1%
1.96
< 0.1%
ValueCountFrequency (%)
1001801
0.3%
99.9108
 
< 0.1%
99.882
 
< 0.1%
99.7110
 
< 0.1%
99.691
 
< 0.1%
99.5129
 
< 0.1%
99.4113
 
< 0.1%
99.3136
 
< 0.1%
99.2108
 
< 0.1%
99.1129
 
< 0.1%

Velocidad_Viento
Real number (ℝ≥0)

ZEROS

Distinct236
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.641592036
Minimum0
Maximum97.6
Zeros12368
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2022-11-06T20:44:59.072192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11.5
median2.3
Q33.5
95-th percentile6
Maximum97.6
Range97.6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.742514137
Coefficient of variation (CV)0.6596454385
Kurtosis68.36427227
Mean2.641592036
Median Absolute Deviation (MAD)1
Skewness2.869468585
Sum1781872.7
Variance3.036355518
MonotonicityNot monotonic
2022-11-06T20:44:59.152952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128253
 
4.2%
226881
 
4.0%
1.721081
 
3.1%
1.820722
 
3.1%
1.920552
 
3.0%
2.219561
 
2.9%
2.119523
 
2.9%
1.619307
 
2.9%
2.319257
 
2.9%
1.518419
 
2.7%
Other values (226)460989
68.3%
ValueCountFrequency (%)
012368
1.8%
0.13546
 
0.5%
0.24157
 
0.6%
0.35082
0.8%
0.45502
0.8%
0.56513
1.0%
0.67534
1.1%
0.78896
1.3%
0.810142
1.5%
0.911316
1.7%
ValueCountFrequency (%)
97.61
< 0.1%
82.31
< 0.1%
821
< 0.1%
802
< 0.1%
741
< 0.1%
69.71
< 0.1%
66.11
< 0.1%
65.21
< 0.1%
651
< 0.1%
61.91
< 0.1%

Radiacion_Solar
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10898
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.8187901
Minimum0
Maximum1104
Zeros334764
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2022-11-06T20:44:59.232440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.8
Q3509.4
95-th percentile898.3
Maximum1104
Range1104
Interquartile range (IQR)509.4

Descriptive statistics

Standard deviation325.4899408
Coefficient of variation (CV)1.313419134
Kurtosis-0.5636332541
Mean247.8187901
Median Absolute Deviation (MAD)1.8
Skewness0.9544236694
Sum167164925.8
Variance105943.7015
MonotonicityNot monotonic
2022-11-06T20:44:59.309509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0334764
49.6%
0.2182
 
< 0.1%
0.1179
 
< 0.1%
0.4173
 
< 0.1%
0.3163
 
< 0.1%
1158
 
< 0.1%
0.7157
 
< 0.1%
0.5153
 
< 0.1%
0.6151
 
< 0.1%
1.1149
 
< 0.1%
Other values (10888)338316
50.2%
ValueCountFrequency (%)
0334764
49.6%
0.1179
 
< 0.1%
0.2182
 
< 0.1%
0.3163
 
< 0.1%
0.4173
 
< 0.1%
0.5153
 
< 0.1%
0.6151
 
< 0.1%
0.7157
 
< 0.1%
0.8140
 
< 0.1%
0.9134
 
< 0.1%
ValueCountFrequency (%)
11041
< 0.1%
1103.91
< 0.1%
1103.81
< 0.1%
1103.21
< 0.1%
1101.51
< 0.1%
1100.81
< 0.1%
1100.71
< 0.1%
1100.21
< 0.1%
1099.51
< 0.1%
1099.31
< 0.1%

Nubosidad
Real number (ℝ≥0)

ZEROS

Distinct1001
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.238278543
Minimum0
Maximum100
Zeros524204
Zeros (%)77.7%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2022-11-06T20:44:59.392798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20.8
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.82839709
Coefficient of variation (CV)3.652680563
Kurtosis29.64148825
Mean3.238278543
Median Absolute Deviation (MAD)0
Skewness5.152592835
Sum2184364.6
Variance139.9109778
MonotonicityNot monotonic
2022-11-06T20:44:59.470156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0524204
77.7%
0.110073
 
1.5%
0.25925
 
0.9%
0.34884
 
0.7%
0.44037
 
0.6%
0.53240
 
0.5%
0.62721
 
0.4%
0.72623
 
0.4%
0.82345
 
0.3%
0.92068
 
0.3%
Other values (991)112425
 
16.7%
ValueCountFrequency (%)
0524204
77.7%
0.110073
 
1.5%
0.25925
 
0.9%
0.34884
 
0.7%
0.44037
 
0.6%
0.53240
 
0.5%
0.62721
 
0.4%
0.72623
 
0.4%
0.82345
 
0.3%
0.92068
 
0.3%
ValueCountFrequency (%)
100363
0.1%
99.973
 
< 0.1%
99.856
 
< 0.1%
99.744
 
< 0.1%
99.644
 
< 0.1%
99.532
 
< 0.1%
99.429
 
< 0.1%
99.335
 
< 0.1%
99.236
 
< 0.1%
99.133
 
< 0.1%

Interactions

2022-11-06T20:44:55.311724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FechaZona_CargaHoraTemperaturaTemperatura_AparentePrecipitacionHumedadVelocidad_VientoRadiacion_SolarNubosidad
02010-01-01Obregon014.713.70.058.92.90.00.0
12010-01-01Obregon114.313.60.061.52.60.00.0
22010-01-01Obregon212.512.10.064.82.50.00.0
32010-01-01Obregon312.111.80.068.82.30.00.0
42010-01-01Obregon410.410.40.073.02.10.00.0
52010-01-01Obregon59.78.90.071.92.00.00.0
62010-01-01Obregon69.18.20.077.82.00.00.0
72010-01-01Obregon78.97.90.079.32.00.00.0
82010-01-01Obregon88.77.60.079.02.00.00.0
92010-01-01Obregon99.19.40.072.62.0120.80.0

Last rows

FechaZona_CargaHoraTemperaturaTemperatura_AparentePrecipitacionHumedadVelocidad_VientoRadiacion_SolarNubosidad
6745352022-10-30Hermosillo1428.930.40.015.00.9682.80.0
6745362022-10-30Hermosillo1529.029.90.013.41.6586.80.0
6745372022-10-30Hermosillo1629.828.50.012.73.6448.00.0
6745382022-10-30Hermosillo1729.026.40.012.52.4275.40.0
6745392022-10-30Hermosillo1827.024.20.017.22.1125.70.0
6745402022-10-30Hermosillo1924.921.50.022.82.07.80.0
6745412022-10-30Hermosillo2022.019.60.033.01.80.00.0
6745422022-10-30Hermosillo2119.118.50.039.81.70.00.0
6745432022-10-30Hermosillo2218.117.90.041.80.30.00.0
6745442022-10-30Hermosillo2318.117.20.046.00.90.00.0